Computerized system and method for identifying members at high risk of falls and fractures

ABSTRACT

Systems and methods for automated interventions to persons identified as being of risk of falling are provided. A subset of members is identified which are associated with at least one of a plurality of falls predictors. At least one falls prediction algorithm is applied to a subset of said medical claims data associated with the subset of members to generate a falls risk score for each of member of the subset. At least one intervention is assigned to each of member of the subset having an assigned risk score above any of several predetermined risk score thresholds which are automatically and electronically initiated based, at least in part, on member data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/743,747, filed Jan. 15, 2020, which is acontinuation of U.S. Non-Provisional patent application Ser. No.14/180,717, filed Feb. 14, 2014, which claims priority to U.S.Provisional patent application Ser. No. 61/777,095, filed Mar. 12, 2013,the disclosures of each of which are hereby incorporated by reference asif fully recited.

BACKGROUND OF THE INVENTION

Unintentional falls are one of the major health risks for adults over 65years of age. Every year one-third of older adults, 65+ years, fall.Falls are indeed the leading cause of injury death in older adults. In2008 alone, 19,700 people died because of injuries resulting fromunintentional falls. And while some falls may only lead to moderate tosevere non-life threatening injuries, the psychological effect of fallsis also severe. Some studies estimated the number of unintentional fallsin 2009 at 2.2 million. Five hundred eighty-one thousand of these fallsresulted in hospitalization. People who have fallen once often develop afear of falling again. This fear leads them to limit their day-to-dayactivities, which further leads to reduced mobility resulting indeteriorating physical fitness level. This in turn puts them at an evengreater risk of falling [1].

Falls (fatal and non-fatal) can be very costly for the health caresystem. According to the numbers reported by CDC, falls among olderadults in 2000 cost the U.S. healthcare system over $19 billion [1].According to another study the cost of fatal fall related injuries in2005 totaled around $349 million: $160 million for men and $189 millionfor women [2]. There is a direct cost related to falls which accountsfor what insurance companies, patients, and health care system pays fortreating fall related fracture/injuries etc., and there is an indirectcost which represents the follow-up long term cost of care. Cost ofhospital care following an injurious fall among the elderly is alsohigher at $6.5 billion as estimated in 2006 by one study [2]. It'sestimated that by 2020, the annual direct and indirect cost of fallinjuries is expected to reach $54.9 billion [1].

Because falls are a high risk for patients due to diminished lifestyle,and health care payers due to monetary implications, it is in the bestinterest of both parties to reduce unintentional falls. However,reducing the number of falls is difficult. Unlike severe medicalconditions such as cancer etc., a fall is not a single medical conditionand as such, does not have a set definition. The current definition of afall that is widely used is “unintentionally coming to rest on theground, floor or other lower level.” Falls result due to multiplemedical conditions that a patient may have or medications that they maybe taking. Most of the current efforts directed toward reducing fallsconsist of questionnaires given to patients at physician visits anddeciding the risk of fall for patients based on these questions. Becausethere is no set definition of a fall, there is also no set “rule ofthumb” questionnaire that could be used as a baseline for predicting therisk of falls for a patient. Multiple studies in the past have shownvarious medical conditions and medications that are connected to fallslike fracture, injuries, difficulty walking, breathing problems, highrisk medications such as benzodiazepines [3], [4].

Although the questionnaire-based methods are widely used to ascertainthe risk of falling for a member, there is no automated “proactive”system that could notify the health care provider or physician about therisk of falls for a person. Patients as well as health care providersand payers can all benefit from such a system because it would reduceexpenditure for avoidable injuries and lead to a better lifestyle forthe patient. There is a need for an automated falls prediction systemand method that can identify the falls risk (probability of fall) foreach patient and further direct them to the proper course ofintervention.

SUMMARY OF THE INVENTION

A computerized system and method according to the present disclosurecomprises a supervised predictive model in order to identify members whoare at-risk of falling, and to estimate their likelihood of fallingduring a specified period (e.g., in the next 12 months). In an exampleembodiment, the automated predictive model is developed using clinicaland non-clinical member-specific data to predict theprobability/likelihood of a member falling within 12 months ofidentification at risk. Multiple medical conditions and/or medicationsare used as triggers to identify at-risk population. These triggers orrisk factors may be used to assign members to relevant clinicalprograms/interventions. In an example embodiment, a computerized systemand method to estimate the fall risk of a member in the next 12 monthsis provided. The system comprises of a set of triggers based on member'sprofile which may include information about a person's medicalconditions, prescriptions, etc. that indicate the member may be at riskfor a fall.

Members with high falls scores are selected for participation in variousexisting clinical programs or special intervention programs are createdfor them in order to help them manage their health and mitigate the riskof falling. Members may be stratified into different risk groups basedon the severity of their likelihood to fall (i.e., high risk score).Different programs are then tailored in order to educate members abouttheir health conditions and provide specific recommendations related tomonitoring their gait, health status, types of medications, follow-upvisits with health care providers etc. Patient compliance withintervention efforts can be monitored to identify those patients thatare at high risk for falling and injuring themselves.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating development and application of aFalls predictive model and model application according to an exampleembodiment;

FIG. 2 is a diagram illustrating development details of a predictivemodel according to an example embodiment;

FIG. 3 is a diagram of triggers or risk factors considered relevant to arisk population of falls according to an example embodiment;

FIG. 4 is a diagram of temporal feature extraction from existing medicalfeatures;

FIG. 5 is a diagram of top predictors that help identify the risk forfalls according to an example embodiment;

FIG. 6 is a score distribution comparison for members that do not and dohave a fall according to an example embodiment;

FIG. 7 a diagram of the distribution of days to fall according to anexample embodiment;

FIG. 8 is a diagram of top predictors that help identify the risk forfalls resulting in emergency room visits according to an exampleembodiment; and

FIG. 9 is an exemplary score data chart.

DETAILED DESCRIPTION

TABLE 1 Glossary Triggers/Risk Medical conditions based on diagnosiscodes, and factors medications or combination of medications which areused to identify an initial set of members for model training. Noteveryone is at a risk of falling so these triggers help identify peoplewho are at some risk of falling. Training Medical conditions used forbuilding the training Triggers population. Model Triggers Medicalconditions used in the model once it has been trained. These may or maynot be the same as the training triggers. Predictors/ Variables createdfor model building/usage from Features different data sources (medicalclaims, pharmacy claims, demographic information, etc.). Some of thetriggers can also be used as predictors so triggers could be considereda subset of predictors. Top Predictors/ Predictors that the modelidentified as the most Features important for identifying who is at ahigh risk of falling. Temporal Variables created for modelbuilding/usage from Features different data sources that capture therelation- ship between time and medical/pharmacy conditions. Risk ScoreA numerical/character score generated by the model representing thelikelihood of a member having a fall in the future. Score Date Date onwhich the model is ran in order to generate a risk score of falling.

In an example embodiment, a predictive model for falls is integrated ina model software application for use by a health benefits provider witha covered patient-member population. The computerized system and methodis helpful in identifying high risk members who will likely fall withina specified period (e.g., one year). Referring to FIG. 1 , a blockdiagram illustrating the process of development and application of afalls predictive model according to an example embodiment is shown.Historical member data, including clinical, medical, and pharmacy claimsdata and consumer data such as demographic data, geographic data andfinancial data 100, is preprocessed and transformed using variouswell-known techniques 102, 104 before input to a predictive model 106.The preprocessing algorithms include variable selections, principlecomponent analysis, and clustering and so on. A falls predictive model108 is developed using a combination of various well-known techniques aslisted in Table 2.

TABLE 2 Predictive Model Techniques Modeling Technique DescriptionDecision Tree-shaped structures that represent sets Tree of decisionswhich generate rules for the classification of a dataset. Logistic Astatistical technique used to find the Regression best-fitting linearrelationship between a target (a categorical variable) and predictors.Artificial Non-linear predictive models that learn Neural throughtraining and resemble biological Networks neural networks in structure.Ensemble Combination of multiple models for consensus prediction withlink functions.

In an example embodiment, the model is a logistic regression model. Theoutput of the predictive model is a risk score that indicates thelikelihood of a member having a fall. The predictive model 108 isincorporated into a model application that is applied to a market-basedmember population 110. Members of the population that are at risk forfalls 112 are selected for proactive clinical interventions 114, such ascase management, emails or letters, for the right course of treatment.Members may be directed to a specific intervention 118 based on whethertheir fall risk exceeds a specified threshold 116. The use of the modelwith proactive clinical programs and interventions helps to improveoutcomes for members and to reduce hospital-related costs for the healthbenefits provider.

With reference to FIG. 1 , multiple data sources 100 are used as inputfor the falls predictive model, including medical information, pharmacyinformation, demographic information, and geographic information. Therisk score of a member can be affected by various factors such as age,gender, previous falls and fractures, medications, and other clinicaldiagnosis. As such the risk score of a member is a culmination ofmultiple factors where some factors are determined to be better atprediction than others.

FIG. 1 also illustrates the data sources 100 and the elements that maycontribute to the patient's fall score. Data sources for an exampleembodiment include membership information, demographic and geographicinformation, clinical and medical claims data, and pharmacy claims data.Those equipped in the art of predictive modeling would know that thesedata sources merely represent an example of the many that can be usedfor predictive modeling and in no way represent any limitations to thescope of this invention. Predictors used as inputs for the predictivemodel such as age, gender, race and states from member profile, clinicaldiagnosis, claims related to previous falls and fractures, andmedications from pharmacy claims data are extracted 104 from these datasources.

The disclosed system and method may be implemented in a single computerenvironment or in a parallelized environment with multiple PC's/Serversperforming varying tasks. This parallel environment could be located atjust one physical space or it may be distributed at multiple remotelocations connected via a computing media including but not limited tosystem bus, processing unit, connector cables etc.

Referring to FIG. 2 , a diagram illustrating development details of apredictive model according to an example embodiment is shown. Asillustrated in FIG. 2 , features extracted from one year historicalmembership and health administrative medical/pharmacy claims data for acovered population is used as input to a predictive modeling system 120.Using the historical member data, the model may be trained on multipleyears of data thereby providing enough information to build a reliablemodel. In an example embodiment, medical, clinical, pharmacy,geographic, and demographic data are extracted from a centralized dataserver for members having at least one condition in a specified period(e.g., one month), and is then preprocessed for generating predictors asinputs of the predictive model. Each individual record represents onemember with his/her statistically significant predictors. The targetevent, in this case falls leading to injury, is extracted from thefuture one year data 122. This database building process simulates areal world scenario in which the model is executed each month and therisk of falling is generated for the future 12 months. This database maybe used for training/validation of the model. The 12 month restrictionon historical data for predictors and future data for the target isexemplary and is not a limitation to the scope of disclosed system andmethod. The fall risk and predictors used to generate that risk may beextracted for N months where N is greater or less than 12.

In the example shown, 1.7 million cases (1,770,610) were considered forbuilding the predictive model. The data consisted of members enrolled inplans from 2009 to 2010. For the disclosed example, these members hadclaims related to one of the twenty-four different medical conditionslisted in FIG. 3 . A random sample of 40% of the data was used to trainthe model, 10% was used to validate the model, and the remaining 50% wasused as the test data set. About 357,929 of the cases had at least onceincidence of a fall in 12 months after the score date giving a targetprevalence of about 20%. Because falls are often underreported in theclaims data, a definition of falls based on a plurality of medicalconditions may be used to implement the disclosed computer-implementedsystem and method. For example, different conditions such asunintentional falls, skull fractures, neck and trunk fracture, upperlimb fracture, lower limb fracture, and dislocation may be used as aproxy for falls from claims data. The presence of these conditions inmember claims and other data served as indicators regarding thelikelihood of a fall occurring within a specified period. While thedisclosed system and method use these six conditions as a proxy forfalls, other conditions may be substituted for the disclosed conditionsor additional conditions may be considered.

Referring to FIG. 4 , a diagram of temporal feature extraction from anexisting feature set is shown. Two different types of temporal featureextraction strategies are explained. In an example embodiment, thehistorical one year data 130 is divided into k non-overlapping periods132. Instead of aggregating the feature value for entire one year, thefeatures are aggregated based on these k periods (S₁, S₂, . . . S_(k)).For example, claims may be aggregated for each month or each quarter ofthe year. In one strategy, weights are assigned to each of thesesegments w₁, w₂, . . . W_(k) 134. The temporal feature (Tmp₁) is thencalculated by aggregating the product of feature value in each segmentwith the segment weight Tmp₁=S₁×w₁+S₂×w₂+ . . . +S_(k)×w_(k) 134. In yetanother strategy, target (Falls/No-falls) 136 is added to the segmenteddataset 138 and a predictive model 140 is fit to this dataset. Rules arethen extracted from the fitted model to transform existing feature intoits temporal version (Tmp₂) 142.

Referring to FIG. 5 , a diagram of variables considered and associatedimportance of falls according to an example embodiment is shown. Some ofthe top predictors were identified and details of the numbers associatedwith the top predictors are shown in the Tables 3, 4, 5, 6 and 7. Theimportance (Chi Square value for category variables and correlationvalue for continuous variables) is a statistical measure to examine therelationship between the individual variable and the dependent variable.The higher value represents more significant relationship.

Top risk factors and some explanation

TABLE 3 Falls Rate by Age Group Age Fall Rate <25 15.77% 26 to 35 17.86%36 to 45 19.22% 46 to 55 19.71% 56 to 65 18.74% 66 to 75 16.57% 76 to 8522.09% >85 31.48% Total 20.21%

TABLE 4 Falls Rate by Gender Gender Fall Rate Female 21.76% Male 17.29%Total 20.21%

TABLE 5 Falls Rate by CMS Risk Score CMS Fall Rate <=0.5 15.61% 0.5 to1.0 17.23% 1.0 to 3.0 23.17% 3.0 to 5.0 30.09% 5.0 to 8.0 34.62%  8.0 to10.0 27.49% >10.0 30.04% Total 20.21%

TABLE 6 Falls Rate by Previous Falls Claim Count Fall Rate 0 17.40 141.88 2 47.05 3 53.17 >3 59.49 Total 20.21%

TABLE 7 Falls Rate by Different Medications Claim Count Fall RateNarcotics 0 15.98 1 20.65 2 24.34 >2 27.31 Chronic Meds 0 22.37 1 17.902 16.75 >2 20.37 Antidepressants 0 18.03 1 22.72 2 24.26 >2 25.10Antihypertensives 0 20.84 1 19.76 2 20.04 >2 18.28

Referring to FIG. 6 , a score distribution comparison for members whohad a fall 150 and those who did not have any fall 12 months after beingidentified at risk of fall 152 according to an example embodiment isshown. As can be seen from the two score distributions in FIG. 6 , thepopulation with a fall within 12 months after score date 152 has higherrisk score compared with the risk score for the population without anyfalls 150 in the 12 months after scoring. Details of the numbersassociated with the risk scores are shown in the Table 8. The mean forthe population with at least one fall is significantly higher at 208.82compared to the mean of population with no fall at 179.27.

TABLE 8 Statistics for the risk scores (Test set only) Event Mean StdDev Minimum Maximum No Fall 179.2765 24.9701 9.5465 274.9344 Having Fall208.8207 32.4518 89.0164 275.3222

Referring to FIG. 7 , a diagram of the distribution of days to eventaccording to an example embodiment is shown. The falls predictive modelselects a group of people who have been identified at risk of fallingbased on one of the 24 triggers. Falls model generates a score for everymember in this list. Based upon the score, a member is placed into anintervention program tailored to assist them. As seen in the example,the distribution of days to event are compared for different risk groupsincluding the top 1%, top 2%, top 3%, top 4%, top 5%, top 10%, and allmembers (overall). Members are scored and then sorted from the highestrisk score to the lowest risk score. Members in top 1% group representthe highest at risk 1% members. A closer look at the distribution ofdays show that the days to fall after score date is very similar for allgroups. This result shows there is equal opportunity for helping membersin different risk groups although the type of intervention programs foreach group could be different.

The disclosed predictive model software application may be accessiblethrough an online server and receive data from a clinical profiledatabase in response to a trigger. A trigger (one of the 24 riskfactors) may be used to invoke the falls predictive model and tocalculate the risk score of a member based on a change in the member'sprofile or clinical data. After a score is calculated, the fallsprobability/score for a member may be used to drive a clinical caresystem used by nurses/clinical specialists to access the member'sclinical profile and claims data. The model can provide informationabout significant predictors for individual members which may be highlycorrelated to the event. Nurses/clinical specialists can then assistpatients in providing the right type of intervention.

Because members fall into various risk groups; top 1%, top 2%, etc.;different risk stratification strategies can be developed based on riskscore range. Table 9 shows some details about the different scoreranges. Various transformation techniques may be applied to change aprobability to a more user-friendly numeric score. As illustrated in thetable, the overall rate of falls is 20%, but the rate of fall in the top1% group is almost three times that rate at 76%. This information isvery important when resources and time are limited. For example, 100,000members may be at risk but only 1% (or 1000) members can be selected foran intervention. If random selection is performed to identify 1000members, only 200 of those at risk of falling may be impacted. However,if model scoring is applied to identify the top 1% high risk members,760 members are impacted. Use of the model results in assistance for anadditional 560 members.

TABLE 9 Risk Stratification % Expected to have a Fall in the next 12Months after Score Population score date Range Top 1% 76.17% >=264 Top2% 73.10% >=258 Top 5% 67.05% >=245 Top 10% 60.03% >=230 Top 20%49.24% >=207 Top 50% 31.79% >=178

Specialized Emergency Room (ER) Falls Model

In another example embodiment, the falls model is used to predict fallsleading to ER visits. The model uses similar triggers as previouslydescribed in the falls prediction model but predicts a risk of fallleading to ER visit by the patient. The output of the predictive modelis a risk score that indicates the likelihood of having a fall resultingin an ER visit. In the example shown, 1.7 million cases (same as fallmodel described previously) were considered for building the predictivemodel. The data consisted of members enrolled in plans from 2009 to2010. These members had claims related to one of the twenty-fourdifferent medical conditions listed in FIG. 3 . A random sample of 40%of the data was used to train the model, 10% was used to validate themodel, and the remaining 50% was used as the test data set. About 51,645of the cases had at least once incidence of fall in 12 months after thescore date giving a target prevalence of about 3.01%. As is evident fromthe statistics the percentage of members with this very specific target,fall resulting in ER visit, is very small compared to all case falls.This result makes the prediction harder. The model building strategy issimilar to the previously described falls prediction model except thetarget population is different. This results in a very specific ER fallsprediction model.

Referring to FIG. 8 , a diagram of variables considered and associatedimportance of ER falls according to an example embodiment are shown.Because both the falls model and the ER falls model use the same triggerpopulation and same predictors, it is expected to see the samepredictors appear in the top predictors list for both models. However,the order/importance of these predictors varies depending upon themodel. For example, ER visits are the top predictor for ER falls but notas important for falls while injury/poisoning count is important forfalls but not equally important for ER falls. An ER falls model may beused to augment an ER strategy, which may be very different from otherintervention strategies.

Just as in the falls model, members may be segmented into various riskgroups: top 1%, top 2%, etc. Different risk stratification strategiescan be developed based on risk score range. Table 10 shows some detailsabout the different score ranges. As illustrated, the overall rate offalls resulting in ER visits is 3%, but the rate of fall in the top 1%group is almost six times that at 18%.

TABLE 10 Risk Stratification for ER Falls Prediction model % Expected tohave a Fall in the next 12 Months after Score Population score dateRange Top 1% 18.86% >=184 Top 2% 15.29% >=174 Top 5% 11.63% >=156 Top10% 9.02% >=148 Top 20% 7.12% >=129 Top 50% 4.67% >=114 Whole riskpopulation 3.01% >=2

While certain embodiments of the present invention are described indetail above, the scope of the invention is not to be considered limitedby such disclosure, and modifications are possible without departingfrom the spirit of the invention as evidenced by the data. One skilledin the art would recognize that such modifications are possible withoutdeparting from the scope of the claimed invention.

Appendix A Temporal Feature Extraction Strategies

This appendix summarizes some Temporal feature extraction strategiesfrom the medical claim features. Because medical claims data is gatheredover a period of time these strategies may be used to transform rawfeatures into temporal features. Ideally, feature value changes overtime such as number of claims, charged amount, number of hospitaladmissions etc.

Strategy 1: In this strategy, a numerical score per feature iscalculated. The strategy can be applied to both aggregate type featureswhere each claim value differs (e.g., Amount of Money paid out) or thecount type features where each claim has a value of 1 or 0 (e.g., BackClaim=1 for Yes and 0 for No, or Radiology Claim=1 for Yes and 0 forNo). This strategy produces a continuous valued temporal feature.

Step-by-Step methodology:

-   -   1. For each feature gather data for the past 1 year from the        score date.    -   2. Divide this complete data range into four intervals/quarters        where each interval represents data for 3 months (4×3=12        Months=1 Year), such as in the chart shown in FIG. 9 .    -   3. Label each interval from 1-4 with the interval closest to the        Score date getting a label of 1 and the interval farthest from        score date getting a label of 4.    -   4. For individual features do the following:        -   a. If the current feature being analyzed is of type            Aggregate, then get a total sum of all the aggregates in            each interval Example:—Assuming 5 claims in 1^(st) interval            of amount 30, 20, 25, 40, and 45 respectively, 3 claims in            2^(nd) interval of amount 25, 30, and 20 respectively, 2            claims in 3^(rd) interval of amount 20 and 40 respectively,            and 1 claim in 4th interval of amount 25. Then the total            sums for each interval are as follows:            -   Interval 1=30+20+25+40+45=160;            -   Interval 2=25+30+20=75;            -   Interval 3=20+40=60;            -   Interval 4=25        -   b. If the current feature being analyzed is of type Count,            then simply get the number of counts in each interval.            -   Example:—Assuming there are 5 claims in 1^(st) interval                of amount 30, 20, 25, 40, and 45 respectively, 3 claims                in 2^(nd) interval of amount 25, 30, and 20                respectively, 2 claims in 3^(rd) interval of amount 20                and 40 respectively, and 1 claim in 4th interval of                amount 25. Then the total sums for each interval are as                follows:            -   Interval 1=5;            -   Interval 2=3;            -   Interval 3=2;            -   Interval 4=1    -   5. Assign weights to each of the four intervals in a decreasing        fashion such that the interval closest to score date gets the        highest weight and the interval farthest from the score date        gets the lowest weight. For example, a weight of 1 for interval        1, ½ for interval 2, ⅓ for interval 3, and ¼ for interval 4.    -   6. Multiply these weights with the respective Sum/Count in each        interval. This gives a weighted Sum/Count for each interval.

Example Type Aggregate: Interval 1=160*1, Interval 2=75*½, Interval3=60*⅓, and

Interval 4=24*¼.

Type Count: Interval 1=5*1, Interval 2=3*½, Interval 3=2*⅓, and Interval4=1*¼

-   -   7. Add the weighted Sum/Count from all the intervals to get a        cumulative Sum.

Example

Type Aggregate: (160*1)+(751/2)+(60*⅓)+(24*¼)=160+37.5+20+6=223.5

Type Count: (5*1)+(3*½)+(2*⅓)+(1*¼)=5+1.5+0.666+0.25=7.416

-   -   8. This cumulative sum (223.5 for Aggregate and 7.416 for Count)        now represents the temporal feature value.

The output of this strategy is a continuous numerical value. People withmost claims closer to a score date will get a higher score compared topeople with most claims farther from score date.

Strategy 2: In this strategy both the magnitude (i.e. the amount/count)and the pattern are taken into account to generate a temporal trend. Thestrategy can again be applied to both Aggregate type features where eachclaim value differs (e.g., Amount of Money paid out) or the Count typefeatures where each claim has a value of 1 or 0 (e.g., Back Claim=1 forYes and 0 for No, or Radiology Claim=1 for Yes and 0 for No). For eachquarter, find the magnitude labeled “high, low, and normal” based on themean of the values in that quarter. The quarters are then transformedinto individual features each having a value of high, low or normal. Adecision tree is built using only these four features and the Target toget branches leading to a target decision. These branches then becomethe new patterns which can be labeled and used as a feature value for asingle categorical temporal feature. Step-by-Step methodology:

-   -   1. For each feature gather data for the past 1 year from the        score date.    -   2. Divide this complete data range into 4 intervals/quarters        where each interval represents data for 3 months (4×3=12        Months=1 Year), such as in the chart shown in FIG. 9 .    -   3. Label each interval from 1-4 with the interval closest to the        Score date getting a label of 1 and the interval farthest from        score date getting a label of 4.    -   4. For individual features:        -   a. If the current feature being analyzed is of type            Aggregate, then get a total sum of all the aggregates in            each interval            -   Example (From Strategy 1):            -   Interval 1=30+20+25+40+45=160;            -   Interval 2=25+30+20=75;            -   Interval 3=20+40=60;            -   Interval 4=25        -   b. If the current feature being analyzed is of type Count,            then get the number of counts in each interval.            -   Example (From Strategy 1):            -   Interval 1=5;            -   Interval 2=3;            -   Interval 3=2;            -   Interval 4=1    -   5. Find the average for each interval over the complete        population.    -   6. Label each interval for the current transaction as high, low,        or normal as follows:        -   If −0.10*X _(i)≤V_(i)−X _(i)≤0.10*X _(i), where X _(i)=mean            of the Interval i, and V_(i)=current value of Interval i,            then label normal.        -   If V_(i)−X _(i)≤−0.10*X _(i), then label low        -   Else label high.    -   7. Create a new Dataset by using these individual as features so        that one feature represents one interval and the value of the        feature represents the value of an interval for a particular        transaction/claim. This gives a dataset with 4 variables.

Person 1 Interval1 Cnt = 5 Interval2 Cnt = 3 Interval3 Cnt = 2 Interval4Cnt = 1 Person 2 Interval1 Cnt = 7 Interval2 Cnt = 9 Interval3 Cnt = 4Interval4 Cnt = 8 Person Interval1 Cnt = . . . . . . . . . . . . . . .

Interval 1 Interval 2 Interval 3 Interval 4 P. No. Cnt Cnt Cnt CntTarget Person 1 High Normal Low Low 1 Person 2 High High Normal High 0 .. . Person n

-   -   8. Add the binary target variable as the 5th variable to this        new dataset.    -   9. Fit a decision tree to this new dataset.    -   10. The branches of the decision trees leading to a decision        form the new temporal patterns.

Use the predicted probability as the new temporal feature value

Since the output of this strategy is a numeric probability, it can beused as a categorical label or a continuous numeric value. This strategyis a modification of the methodology from [5].

REFERENCES

-   [1]    http://www.cdc.gov/HomeandRecreationalSafety/Falls/adultfalls.html.-   [2] Emergency Department Visits for Injurious Falls among the    Elderly, AHRQ Statistical Brief #80, 2006.-   [3] Risk Factors associated with hospitalization for unintentional    falls: Wisconsin hospital discharge data for patients aged 65 and    over, Wisconsin Medical Journal, 2003, Vol. 102, No. 4.-   [4] Medications and Falls in the Elderly: A Review of the evidence    and practical considerations, Pharm. Therapy P&T Journal, November    2003, Vol. 28, No. 11.-   [5] Verduijn M., Sacchi L., Peek N., Bellazzi R., Jonge E. D.,    Mol B. A. J. M. D., “Temporal Abstraction for feature extraction: A    comparative case study in prediction from intensive care    monitoring,” Elsevier Artificial Intelligence in Medicine, Vol. 41,    Issue 1; 2007; pp. 1-12.-   [6] Chung P. V., Anh D. T., “Applying Temporal Abstraction in    Clinical Databases,” IEEE International Conference Research,    Innovation, and Vision of the Future; March 2007; pp. 192-199.-   [7] Kahn M. G., Fagan L. M., Sheiner L. B., “Model-Based    Interpretation of Time-Varying Medical Data,” Proceedings of Annual    Symposium of Computer Applications in Medical Care; November 1989;    pp. 28-32.

What is claimed is:
 1. A system for automatically assigning risk-levelappropriate interventions to persons identified as being of risk offalling, said system comprising: one or more databases, said databasesaffiliated with a medical insurance provider and comprising medicalclaims data for a plurality of members associated with said medicalinsurance provider; one or more computing devices in electroniccommunication with said one or more databases and comprising one or moreprocessors and one or more electronic storage devices, said one or moreelectronic storage devices comprising software instructions, which whenexecuted, configure said one or more processors to: identify a subset ofsaid members associated with at least one of a plurality of fallspredictors; apply at least one falls prediction algorithm to a subset ofsaid medical claims data associated with said subset of said members togenerate a falls risk score for each of said subset of said members;assign at least one of a plurality of interventions to each of saidsubset of said members having an assigned risk score above one of anumber of predetermined risk score thresholds (the “identified at-riskmembers”); and automatically and electronically initiate the assignedinterventions based, at least in part, on member data stored at said oneor more databases for said identified at-risk members.
 2. The system ofclaim 1 wherein: said interventions comprise a phone assessment, anin-home assessment, electronic contact, and enrolment in a clinicalprogram.
 3. The system of claim 1 wherein: said one or more electronicstorage devices comprise additional software instructions, which whenexecuted, configure said one or more processors to generate a graphicalreport comprising identifying information and said risk score for eachof said identified at-risk members.
 4. The system of claim 3 wherein:said graphical report stratifies said identified at-risk members by saidrisk score.
 5. The system of claim 1 wherein: said risk score isspecific to at least age and gender.
 6. The system of claim 1 wherein:said one or more electronic storage devices comprise additional softwareinstructions, which when executed, configure said one or more processorsto evaluate said medical claims data for said plurality of membersassociated with said medical insurance provider to develop said at leastone falls prediction algorithm.
 7. The system of claim 6 wherein: saidat least one falls prediction algorithm is developed using a modelingtechnique comprising one or more of: decision tree, logistic regression,artificial neural networks, and ensemble.
 8. The system of claim 6wherein: said at least one falls prediction algorithm is developed, atleast in part, by: analyzing said medical claims data for an initialsubset of said members having at least one falls model trigger present;pre-processing said medical claims data for said initial subset of saidmembers; extracting features from the medical claims data for saidinitial subset of said members to generate at least one initial fallsprediction algorithm; and providing a plurality of training conditionsto said at least one initial falls prediction algorithm to develop saidat least one falls prediction algorithm.
 9. The system of claim 7wherein: said features are extracted using temporal feature extraction,wherein said falls predictors are selected to correspond with theextracted features; said pre-processing of said medical claims datacomprises one or more of: variable selection, principle componentanalysis, and clustering; and said training conditions comprise one ormore of: an unintentional fall, a skull fracture, a neck fracture, atrunk fracture, an upper limb fracture, a lower limb fracture, and adislocation of bones.
 10. The system of claim 7 wherein: said at leastone falls model trigger comprises one or more of: alcohol abuse,Alzheimer's disease, blood vessel injury, cognitive dysfunction,concussion, dementia, dialysis, difficulty walking, epilepsy andconvulsion, face, eye, or neck contusion, face, neck, or scalp injury,fracture, hallucinations, hip, knee or joint pain, hypotension, motorproblems, muscle weakness, nerve or spinal injury, obesity, one or moreprevious falls, other injury, Parkinson's disease, and stroke.
 11. Thesystem of claim 7 wherein: said falls predictors comprise one or moreof: injury/poisoning incidence count, a lower limb fracture, head, neck,or spine trauma incidence count, previous falls, an upper limb fracture,a neck or trunk fracture, a dislocation, a narcotic prescription, age, ahospital emergency room visit, obesity, governmental agency healthscore, a bone disorder, gender, race, an anti-depressant prescription,and an anti-hypertensive prescription.
 12. The system of claim 6wherein: said one or more databases comprise consumer data for each ofsaid plurality of said members associated with said medical insuranceprovider; and said at least one falls prediction algorithm is developed,at least in part, with said consumer data for said subset of saidmembers.
 13. The system of claim 12 wherein: said consumer datacomprises one or more of: demographic data, geographic data, andfinancial data.
 14. The system of claim 6 wherein: said one or moredatabases comprise pharmacy claims data for each of said plurality ofsaid members associated with said medical insurance provider; and saidat least one falls prediction algorithm is developed, at least in part,with said pharmacy claims data for said subset of said members.
 15. Thesystem of claim 1 wherein: said risk score is configured to reflect arisk of falling within a specified period; and said medical claims datais limited to a predetermined historical period.
 16. The system of claim15 wherein: the specified period is 12 months; and said predeterminedhistorical period is one month prior to a specific date.
 17. A systemfor automatically assigning risk-level appropriate interventions topersons identified as being of risk of falling, said system comprising:one or more databases, said databases affiliated with a medicalinsurance provider and comprising medical claims data for a plurality ofmembers associated with said medical insurance provider; one or morecomputing devices in electronic communication with said one or moredatabases and comprising one or more processors and one or moreelectronic storage devices, said one or more electronic storage devicescomprising software instructions, which when executed, configure saidone or more processors to: identify a subset of said members associatedwith at least one of a plurality of falls predictors within apredetermined historical period; develop at least one falls predictionalgorithm based, at least on part, on a subset of said medical claimsdata associated with said subset of said members within thepredetermined historical period using a modeling technique comprisingone or more of: decision tree, logistic regression, artificial neuralnetworks, and ensemble, wherein said at least one falls predictionalgorithm is configured to reflect a risk of falling within a specifiedfuture period; identify a second subset of said members associated withat least one of a plurality of falls predictors within a secondpredetermined historical period forward in time from said predeterminedhistorical period; pre-process a second subset of said medical claimsdata associated with said second subset of said members utilizing one ormore of: variable selection, principle component analysis, andclustering; apply said at least one falls prediction algorithm to thesecond subset of said medical claims data associated with said secondsubset of said members to generate a falls risk score for each of saidsecond subset of said members reflecting a risk that one of said membersin said second subset falls within the specified future period; assignat least one of a plurality of interventions to each of said secondsubset of said members having an assigned risk score above one of anumber of predetermined risk score thresholds (the “identified at-riskmembers”); generate a graphical report comprising identifyinginformation and said risk score for each of said identified at-riskmembers where said identified at-risk members are stratified intovarious risk groups by said risk score; and automatically andelectronically initiate the assigned interventions based, at least inpart, on member data stored at said one or more databases for said ideidentified at-risk members, wherein said interventions comprise one ormore of: a phone assessment, an in-home assessment, electronic contact,and enrolment in a clinical program
 18. A computer-implemented systemfor identifying a member of a health insurance market-based memberpopulation at risk for falling within a predetermined time period, thesystem comprising: (a) one or more computing devices storing: (1) fallsmodel triggers comprising one or more of: alcohol abuse, Alzheimer'sdisease, blood vessel injury, cognitive dysfunction, concussion,dementia, dialysis, difficulty walking, epilepsy and convulsion, face,eye, or neck contusion, face, neck, or scalp injury, fracture,hallucinations, hip, knee or joint pain, hypotension, motor problems,muscle weakness, nerve or spinal injury, obesity, one or more previousfalls, other injury, Parkinson's disease, and stroke; (2) fallspredictors comprising one or more of: injury/poisoning incidence count,a lower limb fracture, head, neck, or spine trauma incidence count,previous falls, an upper limb fracture, a neck or trunk fracture, adislocation, a narcotic prescription, age, a hospital emergency roomvisit, obesity, governmental agency health score, a bone disorder,gender, race, an anti-depressant prescription, and an anti-hypertensiveprescription; and (b) one or more computing devices executinginstructions to: (1) receive member data and consumer data for theentire health insurance market-based member population, wherein saidmember data comprises data selected from the group consisting of:medical claims data and pharmacy claims data, wherein said consumer datacomprises one or more of: demographic data, geographic data, andfinancial data; (2) analyze said received member data for the entirehealth insurance market-based member population to identify a subset ofmembers within said health insurance market-based member populationhaving one or more of said falls model triggers present in said member'sdata; (3) process the member data and said consumer data for said subsetof members using an algorithm utilizing one or more of: variableselection, principle component analysis, and clustering; (4) extractfeatures from the member data for said subset of members by temporalfeature extraction, wherein said falls predictors are selected tocorrespond with the extracted features; (5) provide a plurality oftraining conditions to a computing device that comprises a fallspredictive model, said training conditions comprising one or more of: anunintentional fall, a skull fracture, a neck fracture, a trunk fracture,an upper limb fracture, a lower limb fracture, and a dislocation ofbones; (6) develop the falls predictive model using a modeling techniquecomprising one or more of: decision tree, logistic regression,artificial neural networks, and ensemble; (7) provide said member dataand said consumer data for said subset of members to the computingdevice that comprises said falls predictive model; (8) receive acalculated falls risk score from the computing device that comprisessaid falls predictive model, wherein said falls risk score representsthe likelihood that the respective member of said subset of members willvisit an emergency room as a result of experiencing a fall within thepredetermined time period, and wherein said calculated falls risk scoreis determined at least in part based on the presence or absence of eachof said falls predictors in said member data for the respective member;(9) sort the received calculated falls risk score into one of aplurality of groups according to a severity level indicated by thereceived calculated falls risk score; (10) assign a clinical program orintervention for each of the members in said subset of members, whereinsaid assignment is determined based on the group into which saidmember's calculated falls risk score has been sorted, where saidintervention is adapted to reduce the member's calculated falls riskscore; and (11) enroll said member in said assigned clinical program orintervention.
 19. The computer-implemented system of claim 18 wherein:the temporal feature extraction is accomplished by executing softwareinstructions which cause the one or more computing devices to: gathermember data for each feature for a time period prior to a date inquestion, wherein said member data comprises a number of events, each ofwhich is associated with a particular time; divide the time period intoa number of equal intervals spanning the time period; sort the gathereddata such that each event is sorted into the interval corresponding withthe particular time for the respective event; sum the data fallingwithin each interval; assign a weighting to each interval in decreasingfashion such that the interval temporally closest to the date inquestion gets the highest weight and the interval temporally farthestfrom the date in question gets the lowest weight; multiply the summedvalue for each interval by the weighting for the respective interval todetermine a weighted sum for each interval; and sum the weighted sums todetermine a cumulative sum, wherein the cumulative sum is utilized todetermine the calculated falls risk score.
 20. The computer-implementedsystem of claim 18 wherein: the temporal feature extraction isaccomplished by executing software instructions which cause the one ormore computing devices to: gather member data for each feature for atime period prior to a date in question, wherein said member datacomprises a number of events, each of which is associated with aparticular time; divide the time period into a number of equal intervalsspanning the time period; sort the gathered data such that each event issorted into the interval corresponding with the particular time for therespective event; fit a predictive model to determine a temporal featurevalue for each extracted feature; and weight each extracted feature withthe respective temporal feature value, wherein the weighted values areutilized to determine the calculated falls risk score.